A genetic algorithm (GA) experiences premature convergence when the diversity is lost in the population. Adaptive GAs aim to maintain diversity in the population by trading off a balance between exploring the problem space and exploiting known solutions. Existing metrics for population diversity measures only examine the similarity between individuals on a genetic level. However, similarities in the order of genes in individuals in ordered problems, such as the travelling salesman problem (TSP) can play an important role in effective diversity measures. By examining the similarities of individuals by the order of their genes, this paper proposes longest common subsequence (LCS) based metrics for measuring population diversity and its application in adaptive GAs for solving TSP. Extensive experimental results demonstrate the superiority of our proposal to existing approaches.